Prediction of Energy Production Level in Large PV Plants through AUTO-Encoder Based Neural-Network (AUTO-NN) with Restricted Boltzmann Feature Extraction

Future Internet, Journal Year: 2023, Volume and Issue: 15(2), P. 46 - 46

Published: Jan. 26, 2023

In general, reliable PV generation prediction is required to increase complete control quality and avoid potential damage. Accurate forecasting of direct solar radiation trends in power production could limit the influence uncertainties on photovoltaics, enhance organizational dependability, maximize utilization factor systems for something such as an energy management system (EMS) microgrids. This paper proposes intelligent level large plants through AUTO-encoder-based Neural-Network (AUTO-NN) with Restricted Boltzmann feature extraction. Here, output may be projected using prior sun illumination meteorological data. The selection modules use AUTO encoder-based Neural Network improve process (AUTO-NN). Machines (RBM) can used during a set regulations development-based proposed model’s result evaluated various constraints. As result, AUTO-NN achieved 58.72% RMSE (Root Mean Square Error), 62.72% nRMSE (Normalized Root 48.04% MaxAE (Maximum Absolute 48.66% (Mean 46.76% Percentage Error).

Language: Английский

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Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead

Energies, Journal Year: 2023, Volume and Issue: 16(10), P. 4060 - 4060

Published: May 12, 2023

Short-term load forecasting (STLF) is critical for the energy industry. Accurate predictions of future electricity demand are necessary to ensure power systems’ reliable and efficient operation. Various STLF models have been proposed in recent years, each with strengths weaknesses. This paper comprehensively reviews some models, including time series, artificial neural networks (ANNs), regression-based, hybrid models. It first introduces fundamental concepts challenges STLF, then discusses model class’s main features assumptions. The compares terms their accuracy, robustness, computational efficiency, scalability, adaptability identifies approach’s advantages limitations. Although this study suggests that ANNs may be most promising ways achieve accurate additional research required handle multiple input features, manage massive data sets, adjust shifting conditions.

Language: Английский

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Evaluating neural network models in site-specific solar PV forecasting using numerical weather prediction data and weather observations

Renewable Energy, Journal Year: 2023, Volume and Issue: 207, P. 266 - 274

Published: March 4, 2023

The effective use of solar photovoltaic (PV) installations implies the integration PV output into overall energy consumption planning, optimization, and control. Moreover, day-ahead trading electricity in Europe makes forecasting utterly important, thus its accuracy becomes particular interest. Data-driven models are typically trained using numerical weather prediction (NWP) data, availability which represents one main obstacles modeling. In this study, we investigate an alternative scenario, artificial neural network (ANN) is on observations then tested NWP data to simulate model's operational forecasting. experiments, historical observations, were collected from three sites eastern Finland. results showed that, although training ANN observational leads a slight decrease performance compared it still outperforms physical model. practice, scenario means that if not available for model training, allow selection parameter tuning, generalization error estimates gradually updated online data.

Language: Английский

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Modelling and real time performance evaluation of a 5 MW grid-connected solar photovoltaic plant using different artificial neural networks

Energy Conversion and Management, Journal Year: 2023, Volume and Issue: 279, P. 116767 - 116767

Published: Feb. 10, 2023

Language: Английский

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Forecasting Solar Photovoltaic Power Production: A Comprehensive Review and Innovative Data-Driven Modeling Framework

Energies, Journal Year: 2024, Volume and Issue: 17(16), P. 4145 - 4145

Published: Aug. 20, 2024

The intermittent and stochastic nature of Renewable Energy Sources (RESs) necessitates accurate power production prediction for effective scheduling grid management. This paper presents a comprehensive review conducted with reference to pioneering, comprehensive, data-driven framework proposed solar Photovoltaic (PV) generation prediction. systematic integrating comprises three main phases carried out by seven modules addressing numerous practical difficulties the task: phase I handles aspects related data acquisition (module 1) manipulation 2) in preparation development scheme; II tackles associated model 3) assessment its accuracy 4), including quantification uncertainty 5); III evolves towards enhancing incorporating context change detection 6) incremental learning when new become available 7). adeptly addresses all facets PV prediction, bridging existing gaps offering solution inherent challenges. By seamlessly these elements, our approach stands as robust versatile tool precision real-world applications.

Language: Английский

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Prediction short-term photovoltaic power using improved chicken swarm optimizer - Extreme learning machine model

Journal of Cleaner Production, Journal Year: 2019, Volume and Issue: 248, P. 119272 - 119272

Published: Nov. 12, 2019

Language: Английский

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Short-term prediction for wind power based on temporal convolutional network

Energy Reports, Journal Year: 2020, Volume and Issue: 6, P. 424 - 429

Published: Dec. 1, 2020

The fluctuation and intermittence of wind power bring great challenges to the operation control distribution network. Accurate short-term prediction for is helpful avoid risk caused by uncertainties powers. To improve accuracy power, temporal convolutional network (TCN) proposed in this paper. method solves problem long-term dependencies performance degradation deep model sequence dilated causal convolutions residual connections. simulation results show that training process TCN very stable it has strong generalization ability. Furthermore, shows higher forecasting than existing predictors such as support vector machine, multi-layer perceptron, long memory network, gated recurrent unit

Language: Английский

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A multi-objective predictive energy management strategy for residential grid-connected PV-battery hybrid systems based on machine learning technique

Energy Conversion and Management, Journal Year: 2021, Volume and Issue: 237, P. 114103 - 114103

Published: April 8, 2021

Language: Английский

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The impact of renewable energy consumption and environmental sustainability on economic growth in Africa

Energy Reports, Journal Year: 2021, Volume and Issue: 7, P. 3877 - 3886

Published: June 29, 2021

In line with the global call for alternative sources of energy rather than conventional fossil-based sources, research in area renewable energy, efficiency, and sustainability seems to have intensified Africa last five years. As a form contribution existing body knowledge, this study seeks parametrically estimate effects consumption environmental on economic growth Africa. Using panel data, thirty-seven African countries, employing system Generalized Method Moments estimation technique which more efficiently solves problems endogeneity omitted variable bias least squares causal method, found that adoption development will lead an increase Africa, both long run short as one percent 0.07% 1.9% increases short-run long-run, respectively The also through reduction emission may not be Africa's priority towards achieving all-inclusive at present because coefficient CO2 is statistically significant. Therefore, countries' governments should intensify efforts developing sector, especially using policy instruments, while harnessing already mature nonrenewable industry rapid continent attainment Agenda 2063.

Language: Английский

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A temporal distributed hybrid deep learning model for day-ahead distributed PV power forecasting

Applied Energy, Journal Year: 2021, Volume and Issue: 304, P. 117704 - 117704

Published: Sept. 1, 2021

Language: Английский

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